Biology subjects

George, A.

Publications and source records attributed to George, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Systematic benchmark of demultiplexing workflows for direct RNA sequencing

Direct RNA nanopore sequencing offers the capacity to directly profile native RNAs, enabling detection of diverse epitranscriptomic modifications based on current changes as the RNA passes through a pore. Innovations in chemistry, hardware and bioinformatic tools have increased sequencing capacity and accuracy, creating demand for sequencing multiple samples within individual runs. Two recent solutions enable barcoding and demultiplexing of samples, but independent comparisons of their performance is limited. Here we benchmark WarpDemuX and SeqTagger using orthogonal ground truths derived from species identity and matched IVT spike-ins across four RNA004 flow cells. Both achieve high demultiplexing precision but differ in their trade-offs in precision and retention. SeqTagger provided strong performance at its recommended settings, whilst WarpDemuX performance varied by model, barcode and read quality. IVT-derived score cutoffs allowed dataset-specific calibration and increased WarpDeMux assignment yield, although precision achieved on IVTs was not consistently reproduced in species reads. These results support matched IVTs as internal controls for guiding choice of demultiplexing settings, while demonstrating the need to validate demultiplexing precision in biological RNA populations.

genomics↗

bioRxiv2026

ONC201 in combination with ATR inhibitor ceralasertib exhibits potent synergy in ovarian cancer cells

Ovarian cancer stands out as one of the most lethal gynecological cancers among women in the United States. While patients with ovarian cancer often show positive responses to treatment, it is common for the cancer to reappear and develop resistance to treatment, so it is important to explore and create novel combinations to increase patient survival rates. ATRi (Ataxia telangiectasia and Rad3-related inhibitors) and ONC201 monotherapies have shown efficacy in ovarian cancer in preclinical models. We hypothesized that combining ATRi with ONC201 would enhance this efficacy. ONC201 induces apoptosis through the TRAIL-pathway following activation of the integrated stress response and inactivation of Akt which is upregulated in ovarian cancer. Six human ovarian cancer cell lines were treated alone and with the novel drug combination of ceralasertib (ATRi) and ONC201. We found an IC50 range of 0.88-20.16 microM for ONC201 and IC50 range of 0.55-54.75 microM for ceralasertib. The combination of ONC201 plus ceralasertib resulted in PARP cleavage consistent with apoptosis along with reduction in Bcl-XL, Xiap and p-Akt expression after 72 hours of treatment. Cytokine profiling showed a decrease in markers of inflammation and tumor progression. Our data provide evidence of synergy from the combination of ATRi ceralasertib and ONC201 against Ovarian cancer cells that merits further investigation including clinical translation.

cancer biology↗

bioRxiv2026

Prenatal alcohol and cannabinoid co-exposure increases ethanol-seeking in adult offspring and produces sex-dependent medial prefrontal cannabinoid receptor 1 expression.

Background: Alcohol and cannabinoids are frequently used during pregnancy, however their combined effects on offspring outcomes are currently under-investigated. In this study, co-exposure was compared to single-drug and drug-free exposures for influences on adult alcohol-seeking and cannabinoid system proteomics. Methods: Pregnant C57BL/6J mice received ethanol vapor (ALC), CP-55,940 (0.75 mg/kg; CB), both (ALC+CB), or neither on gestational days 12-15. Beginning on Postnatal Day 200, male and female offspring underwent home-cage ethanol drinking (N=108) and operant self-administration, progressive-ratio, extinction, and reinstatement testing (N=96). CNR1 and CNRIP1 protein abundance was quantified in the medial prefrontal cortex (mPFC) and dorsomedial striatum (N=57) and compared to within-subject drinking behaviors. Results: Prenatal CB exposure increased cumulative home-cage ethanol intake, primarily in males. At 40% ethanol under progressive-ratio schedules, ALC+CB males consumed more than all other male groups, whereas all exposed female groups consumed more than controls. ALC+CB males also showed greater extinction responding than all other male groups and greater reinstatement intake relative to baseline than CON and ALC males. ALC+CB increased mPFC CNR1 in males relative to all other groups, whereas CB and ALC+CB reduced mPFC CNR1 in females. Higher mPFC CNR1 was associated with fixed-ratio and 40% progressive-ratio intake, extinction, and reinstatement exclusively in males. Conclusions: Prenatal co-exposure produced a persistent male phenotype of enhanced ethanol seeking that was distinct from either exposure alone and accompanied by elevated mPFC CNR1. This behavioral and molecular convergence justifies future investigations manipulating corticostriatal cannabinoid signaling to attenuate exposure-induced ethanol consumption in late adulthood.

developmental biology↗

bioRxiv2026

Guide RNA truncation as a tunable approach for modulating base editing outcomes

Base editing enables programmable nucleotide conversion but can be limited by bystander editing and unintended genomic alterations. Here, we investigated guide RNA (gRNA) truncation as a strategy to modulate base editing outcomes. On editing across multiple genomic loci using ABE8e in HUDEP-2 cells, we found that truncated gRNAs retained measurable editing activity at lengths as short as 15 nucleotides, although activity depended strongly on target sequence, gRNA length, and delivery method. Importantly, truncation altered editing efficiencies at individual nucleotides within the editing window, enabling selective reduction of bystander editing while preserving on-target conversion at several loci, including a therapeutically relevant HbE target. Truncated gRNAs also eliminated detectable large deletions at homologous globin loci, consistent with loss of Cas9 nickase activity at short gRNAs lengths. Similar length dependent effects were observed with cytosine base editing, PAM relaxed ABE8e, and in primary hematopoietic stem and progenitor cells (HSPCs), although the optimal gRNA length varied between contexts. Together, these findings establish gRNA length as an additional parameter for tuning nucleotide level base editing outcomes and reducing bystander editing.

bioengineering↗

bioRxiv2026

Cyanophage CP12 Rewires Host Carbon Regulation through Interface Remodeling and Redox Buffering

Picocyanobacteria drive ocean carbon fixation, and cyanophages reshape host metabolism during infection. In cyanobacteria, the intrinsically disorder Calvin cycle protein 12 (CP12) assembles glyceraldehyde-3-phosphate dehydrogenase (GAP2) and phosphoribulokinase (PRK) into the inhibitory dark complex, yet how phage CP12 homologs modulate redox-sensitive partners remains unclear. Here, we examined viral CP12 across sequence, structure, and post-translational modification (PTM) complexities to resolve the mechanistic remodeling of dark-complex assembly and regulation. Protein-family analysis of CP12 homologs across diverse lineages showed that viral CP12 preserves interface-dominated positions while shifting partner-facing chemistry toward charged and geometry-modulating features. Matched molecular dynamics simulations (MD) of Prochlorococcus MED4 and cyanophage P-HM2 showed that phage CP12 preserves assembly while strengthening PRK-facing contacts and reducing GAP2-facing interface burial. MED4 redox proteomics identified coordinated cysteine oxidation across CP12 and GAP2 under light disturbance, guiding MD to explore thiol-PTM states. Conformational divergence increased with PTM load and localized mainly to GAP2 modifications. Thiol PTM at GAP2 imposed the largest CP12 binding-energy cost, which P-HM2 CP12 buffered, yielding smaller comparative binding-energy penalties than host CP12. These findings link sequence-driven chemistry to interface dynamics and redox PTM responsiveness to light, defining phage CP12 as a regulatory mimetic that may retune host carbon regulation during infection.

microbiology↗

bioRxiv2026

Two-State Effective Connectivity Predicts the effect of Anterior Cingulate Cortex Excitatory / Inhibitory Balance on Posterior Cingulate Cortex

Aims Excitatory/inhibitory imbalance - the equilibrium between glutamatergic excitation and {gamma}-Aminobutyric acid (GABA)ergic inhibition - is implicated in psychosis and may precede psychosis onset. Methods To investigate whether excitatory/inhibitory balance relates to subclinical psychotic experiences, anxiety and depression, we developed a network-level measure integrating Magnetic Resonance Spectroscopy Glutamate/GABA ratios with effective connectivity during resting-state fMRI. In 20 healthy young adults (aged between 18-32; 50% female), we first measured excitatory/inhibitory balance, operationalised as Glutamate/GABA and Glutamate+Glutamine(Glx)/GABA ratio, in the dorsal anterior cingulate cortex. Second, we applied Two-state Dynamic Causal Modelling to assess effective connectivity of the default-mode network, using these ratios as empirical priors to determine how they translate into network connectivity. Third, we ran Bayesian Model Selection at the model and family levels to identify the optimal models. Results The two-state model with Glx/GABA ratio as direct input into the posterior cingulate cortex best explained the data. This best-fitting model incorporated the neurometabolite ratio with Glutamate and GABA and used two-state connectivity. Conclusions Excitatory/inhibitory balance underlies resting-state effective connectivity, with the Glx/GABA ratio shaping connectivity networks. This pathway highlights excitatory/inhibitory balance as a predictor of two-state brain networks, potentially aiding identification of high-risk individuals and informing new interventions.

neuroscience↗

bioRxiv2026

ChatGEM: An Agentic Architecture Enabling Interactive Simulation of Genome-Scale Metabolic Models

Genome-scale metabolic models (GEMs) are powerful tools for predicting cellular phenotypes and guiding microbial strain engineering, yet broad adoption remains challenging due to the computational expertise required. To overcome that, we present ChatGEM, an agentic platform that enables interactive GEM simulation through natural language. Built on the multi-agent ADEPT framework, ChatGEM integrates COBRApy within a retrieval-augmented generation (RAG) architecture that coordinates code generation and execution through specialized agents. Benchmarking across three tasks of increasing complexity showed that RAG-enabled code generation improved the mean overall performance score from 2.63 to 4.20 while reducing the execution time significantly starting from routine to complex tasks. Application of ChatGEM using an enzyme-constrained GEM (ecGEM) for four engineered Pseudomonas putida KT2440 strains identified the constitutive strain as the optimal chassis for succinate overproduction using a succinate leakage index - a prediction observed experimentally. Therefore, ChatGEM democratizes metabolic modeling by enabling researchers without computational expertise to perform sophisticated GEM-based analyses through natural language, and, hence, accelerating scientific discovery.

systems biology↗

bioRxiv2026

Functional High Throughput Drug Screening Reveals Cyproheptadine as a Novel Treatment for LMNA -related Cardiomyopathy

ObjectivesTo define shared and variant-specific mechanisms underlying LMNA-associated dilated cardiomyopathy (DCM) and identify therapeutic candidates using human stem cell-based models. BackgroundVariants in the gene LMNA, encoding lamin A/C, cause 5-10% of dilated cardiomyopathies (DCM) and are strongly associated with heart failure and arrhythmias. Yet, the mechanisms by which LMNA variants drive disease and the distinction between shared and variant-specific phenotypes remain unclear. MethodsTo address this, we generated human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) from six LMNA-DCM patients carrying three pathogenic variants (T150A, E381Afs*39, R527H) and from five healthy control patients. ResultsLMNA hiPSC-CMs exhibited nuclear membrane deformation, reduced beat rate, arrhythmias, and prolonged calcium transients. Transcriptomic and electrophysiological analyses revealed downregulation of cardiac genes and ion channels, with abnormal Ca{superscript 2} handling emerging as a shared disease mechanism. Leveraging a high-throughput functional assay, we performed an unbiased drug screen and identified cyproheptadine, an FDA-approved antihistamine, as the only compound to alleviate abnormal function across all LMNA variants. ConclusionOur findings reveal a shared disease mechanism across multiple LMNA variants driven by dysregulated Ca{superscript 2} handling. This work establishes a patient-specific drug discovery platform and identifies cyproheptadine as a promising therapeutic candidate for LMNA-associated dilated cardiomyopathy. HighlightsO_LIPatient-specific LMNA hiPSC-cardiomyocytes robustly recapitulate disease phenotypes, including nuclear defects, arrhythmias, and contractile dysfunction. C_LIO_LIDysregulated calcium handling emerges as a unifying mechanism driving pathology across distinct LMNA variants. C_LIO_LIVariant-resolved analysis reveals both shared and mutation-specific molecular and functional signatures. C_LIO_LIHigh-throughput screening identifies cyproheptadine as a potent, broadly effective rescue agent across all tested LMNA variants. C_LIO_LIThis work establishes a scalable precision medicine platform for rapid therapeutic discovery in inherited cardiomyopathies. C_LI

Cell Biology↗

bioRxiv2026

Targeting melanosome pH is an effective method for the treatment of oculocutaneous albinism

Oculocutaneous albinism (OCA) is a genetic condition associated with impaired visual acuity and increased skin cancer risk. When OCA is due to defects in melanosome ion transport, abnormally acidic conditions in the melanosome lumen inhibit tyrosinase, the critical pigment synthetic enzyme. Hence, a therapeutic approach that optimizes melanosome pH to increase pigment production presents a potential treatment for OCA and a method for decreasing skin cancer risk. Here, we report that reduction in sAC (ADCY10) activity via naturally occurring human variants in ADCY10 restores OCA pigmentation, and sAC inhibition increases melanin synthesis in both human and mouse OCA models. These findings demonstrate that targeting melanosome pH is an effective, previously untapped therapeutic strategy for OCA and elevated skin cancer risk.

physiology↗

bioRxiv2026

An LLM-driven pipeline for proteomics-based detection and structural modeling of post-translational modifications

Post-translational modifications (PTMs) on proteins dynamically regulate their functions and subsequently cellular physiology. Significant advances have been made in their detection and modeling: mass spectrometry-based proteomics has become the cornerstone for PTM detection in complex samples, while emerging structure-prediction frameworks enable modeling of PTM-dependent conformational changes. However, the biological significance of many PTMs remains largely unexplored, in part because integrated pipelines that bridge PTM detection with structural modeling remain limited. We present a generative AI-driven pipeline that integrates PTM detection with structural modeling of their effects on protein dynamics and interactions. The pipeline comprises two complementary tools: PTMdiscoverer and PTM-Psi. First, PTMdiscoverer leverages large language models to identify, annotate, and interpret candidate PTMs from open-search proteomics results, addressing limitations of conventional proteomics tools. Next, PTM-Psi models the structural, functional, and dynamic consequences of these spatially aware modifications on protein dynamics. These two components bridge PTM discovery with mechanistic interpretation at the structural level. We demonstrate our pipeline by using cyanobacterial proteomics data to study potential molecular mechanisms of redox-regulated "dark complex" formation in carbon metabolism, advancing our ability to interpret PTM-mediated regulation in microbial systems.

bioinformatics↗

bioRxiv2026

A Systematic Approach Toward Implementing Machine Learning Techniques to Analyze Gut Microbiome Data

This study investigates the relationship between the gut microbiota and specific diseases. Data was collected from the Human Gut Microbiome Atlas, which examines regional variations across 20 countries on five continents, categorizing microbial species by taxonomy, from genus to species. The Atlas provides color-coded phylum classifications, numerical species counts within the same genus, and an analysis of dysbiosis-related associations with 23 diseases, as well as region-enriched species. The data stratified samples into distinct categories such as westernized, non-westernized, cancerous, and non-cancerous. The findings demonstrate that tree-based ensemble methods, such as Bagging and Boosting prediction methods, achieved the highest accuracies across all categories due to their robustness in handling the complex, high-dimensional data. The XGBoost model yielded the strongest predictive performance, achieving 91% accuracy for westernized cancer-associated samples, 84% accuracy for non-westernized cancer-associated samples, 92% accuracy for westernized samples, and 78% for non-westernized samples. Additionally, advanced topological data analysis was used to assess the global structure and underlying patterns within the dataset. ImportanceThis research aims to connect gut microbiome composition to diseases using global datasets from the Human Gut Microbiome Atlas. The goal was to evaluate how accurately different machine learning algorithms could classify microbiota species and diseases and predict disease associations by comparing westernized and non-westernized populations, including both cancerous and noncancerous groups. These findings can contribute to the future creation of population-specific and disease-specific microbial models.

bioinformatics↗

bioRxiv2026

Rab12 is a regulator of mitophagy and mitochondrial homeostasis

Rab GTPases orchestrate vesicular trafficking, but their contributions to mitochondrial quality control are not fully defined, despite links to multiple mitochondria-related human diseases. We conducted a family-wide siRNA-based screen using mt-mKeima/YFP-Parkin HeLa cells to identify regulators of depolarization-induced mitophagy. The screen identified several candidate Rabs, and follow-up studies validated Rab12 as a negative regulator of mitophagy. Rab12 knockdown or knockout augments clearance of damaged mitochondria basally and/or after FCCP-induced depolarization, with findings reproduced across distinct cell types. Rab12 depletion increased mitochondrial content, lowered mitochondrial membrane potential, and reduced mitochondrial DNA damage, without detectable changes in overall cellular bioenergetic capacity. Together, these results indicate that Rab12 restrains mitophagic engagement and its loss permits accumulation of lower-functioning mitochondria that are hypersensitive to mitophagy-inducing stress. Rab12 thus emerges as a novel effector linking vesicular trafficking machinery and mitochondrial homeostasis, with potential implications for neurodegenerative disorders and other Rab-associated diseases.

cell biology↗

bioRxiv2026

Disordered N-terminal region of CLK1 oligomerizes for recruitment to nuclear substructures and splicing function

Cdc2-like kinase 1 (CLK1) phosphorylates the serine-arginine (SR) proteins, a family of nuclear factors essential for mRNA splicing. The ability of CLK1 to recognize and efficiently modify SR proteins is strictly dependent on a lengthy, disordered N-terminus flanking its kinase domain. In addition to stimulating phosphorylation, this 150-residue extension also induces large oligomer formation in CLK1 but it is unclear whether said structure is important for catalytic or cellular function. We identified a subset of N-terminal residues that, upon removal, impairs oligomerization of CLK1 but does not abolish phosphorylation of the SR protein SRSF1. Despite robust phosphorylation, CLK1 lacking these high-order oligomerization sequences cannot effectively enter nuclear speckles and release SRSF1. This inability to mobilize CLK1 has detrimental effects on the alternative splicing of the CLK1 gene, severing an important, autoregulatory mechanism that controls active cellular levels of the kinase. Such findings indicate that whereas a limited group of residues in the N-terminus activates the kinase domain for SR protein phosphorylation, sequences that induce oligomerization direct CLK1 to the proper subnuclear structures for splicing function.

biochemistry↗

bioRxiv2026

Spatiotemporal 4D Whole-cell Modeling of a Minimal Autotroph Reveals Central Carbon Metabolism Regulated Locally by Protein Megacomplexes via Post-translational Modifications under Light Disturbance

Photosynthetic microorganisms rely on multiple pathways in central carbon metabolism to adapt to fluctuating light and energy availability across diel cycles. Mechanistic insight into the regulatory dynamics of this adaptation requires integrating processes spanning disparate timescales, from rapid redox-dependent post-translational modifications (PTMs) to slower changes in protein expression and metabolic pathway usage. To address this complexity beyond genome-based inference and traditional modeling, we develop a whole-cell four-dimensional (3D + time) model of the marine cyanobacterium Prochlorococcus marinus MED4 that explicitly represents the spatial organization of enzymatic and molecular processes in central carbon metabolism under light perturbation. We employ a perturbation-based research design to experimentally generate time-series, multi-omics measurements that provide molecular descriptors and cryo-ET images as constraints for this dynamic 4D framework. The integration of experiments and modeling across defined light regimes enables quantitative validation of system-level responses and forecasting under distinct light disturbances. We test the hypothesis that light-dependent redox PTMs regulating the structural assembly of a protein megacomplex, the "dark complex," modulate metabolic flux at a conserved regulatory node of the Calvin-Benson cycle (CBC) in cyanobacteria. Our model shows that subcellular spatial organization buffers rapid light-induced changes in thylakoid reaction rates, which are followed by redox-PTM-mediated sequestration or release of CBC enzymes in the dark complex, ultimately impacting carbon fixation dynamics within carboxysomes. Comparison with an equivalently parameterized well-mixed stochastic model demonstrates that post-translational regulation not only buffers transcriptional noise and diffusion-driven fluctuations but also stabilizes phenotypic outcomes, underscoring the importance of spatial heterogeneity in phenotypic robustness. This ability to probe adaptive, spatiotemporally resolved mechanisms in photosynthetic machinery and central carbon metabolism addresses a critical gap in genotype-to-phenotype inference and expands modeling and design capabilities for understudied or genetically intractable autotrophs such as P. marinus MED4. Significance StatementThis work advances 4D whole-cell modeling by presenting the first spatiotemporal simulation of a photosynthetic autotroph using the Lattice Microbes platform. Using Prochlorococcus marinus MED4, we show that subcellular spatial organization of organelles, diffusion constraints, and redox regulation collectively shape central carbon metabolism across orders of magnitude in space and time. Through a perturbation-based strategy that generates multi-omics data sets over time, we construct and validate a spatially and temporally resolved model of MED4, constrained by high-resolution (10 nm) cryo-electron tomography. Our results highlight the importance of localized biochemical reactions and redox-dependent post-translational modification of enzymes in regulating carbon fixation in a noisy environment under light disturbance. This study establishes a spatiotemporal, whole-cell physiology modeling framework as a transformative tool for uncovering multiscale regulatory responses to environmental gradients.

systems biology↗

bioRxiv2025

EcoKMER: One-stop shop for spatio-temporal metagenomic exploration using DataFed

Spatially distributed environmental sampling generates highly complex and multidimensional datasets illuminating key insights into microbial diversity, evolutionary-coevolutionary processes, and host-pathogen interactions. While these sampling methods generate high value datasets, dataset size, the dataset integration, visualization, analysis, and provenance tracking present significant bottlenecks to scientific discovery. To address this bottleneck, we developed EcoKMER, an R-Shiny front-end application designed to streamline metagenomic data accessibility and provide geospatial context to data in support of hypothesis-driven investigations into environmental sampling, supported by DataFed as its back-end data management platform. EcoKMER enables interactive visualization and filtering of harmonized metagenomic data and metadata using an interoperable approach, allowing users to extract spatially distributed sample-based metadata on top of environmental parameters such as geolocation, temperature, pH, for investigating ecological changes across time and space enhancing sample processing methods. As an example, we deployed this tool to track analysis of metagenomes from the organisms in the Salish Sea Estuary, consistent with existing community-accepted standards. Built on top of DataFed, a flexible and robust scientific data management system built for data lakehouse architectures, EcoKMER is positioned as a powerful tool to improve sampling strategy decision making, accelerate new insights for collaborative biological and environmental research, and fostering AI-ready analyses designed to enhance discovery and guidance for bioeconomic engineering. The published manuscript is available at https://www.osti.gov/biblio/3412938

bioinformatics↗

bioRxiv2025

Extracellular Vesicle Gene Expression Enables Sensitive Detection of Colorectal Neoplasia

BACKGROUND & AIMSExtracellular vesicles (EVs), including exosomes, are emerging as promising carriers of disease-specific biomarkers due to their molecular cargo reflective of cellular origin. While cell-free DNA (cfDNA) methylation assays have recently been developed for colorectal cancer (CRC) screening and perform well for cancer detection, they show limited sensitivity for advanced adenomas (AA), a key precursor in the CRC pathway. To address this gap, we sought to determine whether other blood-based analytes, specifically EV-derived long and small RNA transcriptomes and EV proteomes, could improve detection of AA and early-stage CRC. In a prospective cohort, we performed a head-to-head comparison of EV transcriptomics, EV proteomics, and their combinations against cfDNA methylation, all measured from the same patient cohort, enabling a direct performance benchmark and identification of the most promising modality for larger-scale CRC screening studies. METHODSWe prospectively collected pre-colonoscopy plasma samples from 220 participants across three clinical sites. EVs were isolated and profiled using long RNA-seq, small RNA-seq, and Olink-based proteomics. cfDNA was analyzed for methylation patterns. Analyses were conducted according to a statistical analysis plan pre-specified before unblinding. Machine learning models were developed under nested cross-validation to evaluate sensitivity for detecting AA and CRC at a fixed specificity of 91%, consistent with clinical screening benchmarks. RESULTSEV-derived gene expression on long RNA demonstrated the highest sensitivity for detecting AA as well as CRC: 54.8% (95% CI, 26.4%-75.6%) for AA and 94.1% (95% CI, 79.2%-100%) for CRC. This outperformed cfDNA methylation (33.6% [95% CI, 9.7%-60.3%] for AA, 81.3% [95% CI, 64.6%- 93.9%] for CRC) and other EV-based modalities. In addition, for small RNA the sensitivities were 42.6% (95% CI, 35.9%-47.4%) for AA, and 78.9% (95% CI, 69.2%-83.0%) for CRC, while for proteomics the sensitivities were 30.0% (95% CI, 13.9%-40.0%) for AA, and 64.2% (95% CI, 43.6%-87.2%) for CRC. Transcriptomic profiles revealed progressive enrichment of hallmarks of cancer pathways, including apoptosis and epithelial-mesenchymal transition, across disease stages. Multiomic integration did not improve performance beyond EV transcriptomics alone. CONCLUSIONSBy directly comparing multiple EV-based and cfDNA analytes within the same patient cohort, we found that EV transcriptomics delivers the strongest diagnostic performance for both AA and CRC. This rigorous benchmarking approach allows clear prioritization of the most promising modality guiding the design of larger validation studies and accelerating development of next-generation, blood-based CRC screening tools.

genomics↗

bioRxiv2025

Salivary Extracellular Vesicle RNA Profiling Reveals Biomarkers for Sjogrens

Sjogrens is a chronic autoimmune disease affecting exocrine glands and is subclassified into SSA-positive (SSA+) and SSA-negative (SSA-) subtypes, with a complex diagnostic journey and an average diagnostic delay of almost 4 years. While SSA+ cases can be detected via serological testing, current assays lack specificity. For SSA-patients, no non-invasive diagnostic tools exist, and diagnosis often requires invasive lip biopsy. A saliva-based liquid biopsy capable of diagnosing both subtypes is therefore of high clinical interest. However, saliva poses challenges due to its abundant oral microbiome, which complicates unbiased biomarker discovery. In this study, we present a novel RNA sequencing workflow that efficiently depletes microbial content, enabling deep profiling of long RNAs within salivary extracellular vesicles (EVs). This approach identified both known and novel RNA biomarkers capable of diagnosing SSA+ and SSA-subtypes with high sensitivity and specificity. Moreover, we uncovered distinct RNA signatures that allow molecular stratification of Sjogrens subtypes. Pathway analysis in SSA+ cases revealed enrichment of immune and glandular pathways consistent with prior tissue-based studies, supporting the utility of salivary EVs as a non-invasive surrogate for tissue biopsy. Importantly, our data provides new molecular insights into the under-characterized SSA-subtype, laying the foundation for future mechanistic studies and facilitating their broader inclusion in clinical trials.

genomics↗

bioRxiv2025

Deep Profiling of EV Long RNAs Reveals Biofluid-Specific Transcriptomes and Splicing Landscapes

RNA profiling of extracellular vesicles (EVs) from human biofluids has historically been limited to small RNA species, with long RNAs--such as mRNA exons and long non-coding RNAs--remaining largely underexplored. Moreover, the dominance of hematopoietic-derived EVs in complex fluids like plasma has posed significant challenges for detecting low-abundance, tissue-specific transcripts. Here, we establish foundational transcriptomic maps of long RNAs in EVs from plasma, urine, and cerebrospinal fluid (CSF) using ultra-deep whole transcriptome sequencing (WTS), revealing both fluid-specific and shared expression and splicing signatures. We then introduce a targeted RNA capture method that enriches for all protein-coding and long non-coding transcripts, dramatically enhancing sensitivity for gene and splice variant detection. Applying this approach to brain-specific transcripts, we achieve >85-fold enrichment of target gene expression and, on average, 3.1-fold increase in detected splice junctions per gene compared to untargeted WTS. As a proof of concept, we apply this brain-targeted RNA panel to EVs from plasma in a Parkinsons disease cohort of 40 plasma samples and compare its performance to exome sequencing as well as untargeted WTS. This work advances EV transcriptomics into the long RNA domain and establishes a framework for high-sensitivity, noninvasive biomarker profiling across tissues and diseases.

genomics↗